Monica Dessole

Researcher in High-Performance Scientific Computing

I am a research fellow at CERN SFT-EP working in the ROOT group. My research focuses on numerical and computational linear algebra, and it includes the development of efficient algorithms for scientific computing and the implementation of scalable, reusable and maintainable scientific software, primarily parallel solvers for differential equations, and methods for analyzing large matrices and datasets.

monica.dessole at

Short Bio

I received my PhD in Computational Mathematics at the University of Padova, where I also held a one year research fellowship year at the Department of Information Engineering. Before joining CERN, I was part the HPC/Cloud group at Leonardo Labs, where I was involved in the design and implementation of cloud stacks for Big Data analytics. Previously, I have worked on the massively parallel solution on GPUs of linear systems arising from differential equations such as high-fidelity numerical simulations of incompressible unsteady Navier-Stokes equations and BVODE problems. In particular, I have explored the performance attained by the approximate iterative solution on GPUs of sparse triangular linear systems in the context of ILU preconditioning with application to immiscible dual fluid flow simulations. Moreover, I delepoped a direct GPU solver taylored for structured matrices arising e.g. from the discretization of two point boundary value problems. I have also dealt with the efficient solution of ill-posed problems my means of low-rank models and statistical techniques with application to analysis of datasets in high dimensional spaces. I developed a block pivoting technique which can be adopted to increase performance in rank deficient QR computations and in sparse recovery problems.

For further information please check out my cv.


MOOC: Scientific Computing in Python (in Italian)
Introduction to Python ( Italian version )


LHDM - Active-set solver for NonNegative Least Squares based on Deviation Maximization column selection
QRDM - QR factorization for rank-deficient matrices based on Deviation Maximization column selection
dCATCH - Nearly-optimal polynomial regression designs on high-dimentional point clouds
PARASOF - Direct GPU solver for linear systems with BABD structure
ns2div - Hybrid CPU-GPU solver for Navier-Stokes equations with variable density


Journal Papers

  • M. Dessole, F. Marcuzzi
    Accurate detection of hidden material changes as fictitious heat sources from thermographic data
    Numerical Heat Transfer, Part B: Fundamentals, 2023
  • M. Dessole, M. Dell’Orto, F. Marcuzzi
    The Lawson-Hanson Algorithm with Deviation Maximization: Finite Convergence and Sparse Recovery
    Numerical Linear Algebra with Applications, 2023
  • M. Dessole, F. Marcuzzi
    Deviation Maximization for Rank-Revealing QR Factorizations
    Numerical Algorithms, 2022.
  • M. Dessole, F. Marcuzzi, M. Vianello
    dCATCH-A Numerical Package for d-Variate Near G-Optimal Tchakaloff Regression via Fast NNLS
    Mathematics, 2020
  • M. Dessole, F. Marcuzzi
    A massively-parallel algorithm for Bordered Almost Block Diagonal systems on GPUs
    Numerical Algorithms, 2020
  • M. Dessole, F. Marcuzzi, M. Vianello
    Accelerating the Lawson-Hanson NNLS solver for large-scale Tchakaloff regression designs
    Dolomites Research Notes on Approximation, 2020
  • M. Dessole, F. Marcuzzi
    Fully iterative ILU preconditioning of the unsteady Navier-Stokes equations for GPGPU
    Computers & Mathematics with Applications, 2019

PhD thesis